print("HELLO,WE ARE TRYING TO MAKE A MODEL TO FIND BEST LAPTOP IN BUDGET")

HELLO,WE ARE TRYING TO MAKE A MODEL TO FIND BEST LAPTOP IN BUDGET

import pandas as pd

import numpy as nup

filepath2 = r"C:\Users\91623\OneDrive\Desktop\data12.txt"
df1= pd.read_csv(filepath2,delimiter='\t')
print(df1)

             laptop processor_gnrtn           processor  ram_gb   ssd  hdd  \
0        HP Celeron         Missing  Intel Celeron Dual       8   256    0   
1       DELL Vostro         Missing         AMD Ryzen 5       8   256    0   
2     DELL Inspiron         Missing         AMD Ryzen 5       8   256    0   
3    Infinix INBook            10th       Intel Core i5       8   512    0   
4       acer Aspire         Missing         AMD Ryzen 5       8   512    0   
..              ...             ...                 ...     ...   ...  ...   
406   Lenovo Legion            10th       Intel Core i7      16  1024    0   
407      APPLE 2021         Missing        Apple M1 Pro      16  1024    0   
408     MSI Stealth            12th       Intel Core i7      32  1024    0   
409        ASUS ROG            12th       Intel Core i9      32  1024    0   
410      APPLE 2021         Missing        Apple M1 Max      32  1024    0   

          os  graphic_card_gb  Unnamed: 8  latest_price  old_price  \
0    Windows                0         NaN         32490      39288   
1    Windows                0         NaN         41990      59580   
2    Windows                0         NaN         41990      49613   
3    Windows                0         NaN         42490      59999   
4    Windows                0         NaN         43990      52999   
..       ...              ...         ...           ...        ...   
406  Windows                8         NaN        234990     349890   
407      Mac                8         NaN        243990     259900   
408  Windows                8         NaN        279990     313990   
409  Windows                8         NaN        299990     376990   
410      Mac                8         NaN        309990     329900   

     star_rating  ratings  reviews  Unnamed: 14  Unnamed: 15  
0            4.0       58        6          NaN          NaN  
1            4.3      939      107          NaN          NaN  
2            4.4      649       84          NaN          NaN  
3            4.1      383       65          NaN          NaN  
4            0.0        0        0          NaN          NaN  
..           ...      ...      ...          ...          ...  
406          0.0        0        0          NaN          NaN  
407          4.8       42        8          NaN          NaN  
408          0.0        0        0          NaN          NaN  
409          0.0        0        0          NaN          NaN  
410          5.0        3        1          NaN          NaN  

[411 rows x 16 columns]

df1.columns

Index(['laptop', 'processor_gnrtn', 'processor', 'ram_gb', 'ssd', 'hdd', 'os',
       'graphic_card_gb', 'Unnamed: 8', 'latest_price', 'old_price',
       'star_rating', 'ratings', 'reviews', 'Unnamed: 14', 'Unnamed: 15'],
      dtype='object')

df1.head(10)

	laptop	processor_gnrtn	processor	ram_gb	ssd	hdd	os	graphic_card_gb	Unnamed: 8	latest_price	old_price	star_rating	ratings	reviews	Unnamed: 14	Unnamed: 15
0	HP Celeron	Missing	Intel Celeron Dual	8	256	0	Windows	0	NaN	32490	39288	4.0	58	6	NaN	NaN
1	DELL Vostro	Missing	AMD Ryzen 5	8	256	0	Windows	0	NaN	41990	59580	4.3	939	107	NaN	NaN
2	DELL Inspiron	Missing	AMD Ryzen 5	8	256	0	Windows	0	NaN	41990	49613	4.4	649	84	NaN	NaN
3	Infinix INBook	10th	Intel Core i5	8	512	0	Windows	0	NaN	42490	59999	4.1	383	65	NaN	NaN
4	acer Aspire	Missing	AMD Ryzen 5	8	512	0	Windows	0	NaN	43990	52999	0.0	0	0	NaN	NaN
5	DELL Inspiron	Missing	AMD Ryzen 5	8	256	0	Windows	0	NaN	44390	56573	4.0	276	24	NaN	NaN
6	DELL Inspiron	Missing	AMD Ryzen 5	8	256	0	Windows	0	NaN	44490	58194	4.2	14	1	NaN	NaN
7	DELL Vostro	Missing	AMD Ryzen 5	8	256	0	Windows	0	NaN	44490	58286	4.3	8	2	NaN	NaN
8	acer Aspire	Missing	AMD Ryzen 5	8	512	0	Windows	0	NaN	44990	56990	4.4	313	39	NaN	NaN
9	Nokia PureBook	10th	Intel Core i5	8	512	0	Windows	0	NaN	45990	74990	4.7	6	1	NaN	NaN

df1.sort_values('graphic_card_gb')

	laptop	processor_gnrtn	processor	ram_gb	ssd	hdd	os	graphic_card_gb	Unnamed: 8	latest_price	old_price	star_rating	ratings	reviews	Unnamed: 14	Unnamed: 15
0	HP Celeron	Missing	Intel Celeron Dual	8	256	0	Windows	0	NaN	32490	39288	4.0	58	6	NaN	NaN
154	DELL Vostro	11th	Intel Core i5	8	512	0	Windows	0	NaN	64190	85391	0.0	0	0	NaN	NaN
155	Lenovo IdeaPad	11th	Intel Core i5	16	512	0	Windows	0	NaN	64990	97890	0.0	0	0	NaN	NaN
156	ASUS VivoBook	11th	Intel Core i5	16	256	1024	Windows	0	NaN	64990	85990	4.7	10	1	NaN	NaN
157	ASUS VivoBook	11th	Intel Core i5	16	256	1024	Windows	0	NaN	64990	85990	4.3	328	51	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
401	APPLE 2021	Missing	Apple M1 Pro	16	1024	0	Mac	8	NaN	225990	239900	4.8	42	8	NaN	NaN
400	Lenovo Core	10th	Intel Core i7	16	512	0	Windows	8	NaN	211753	214760	0.0	0	0	NaN	NaN
409	ASUS ROG	12th	Intel Core i9	32	1024	0	Windows	8	NaN	299990	376990	0.0	0	0	NaN	NaN
403	APPLE 2021	Missing	Apple M1 Pro	16	1024	0	Mac	8	NaN	225990	239900	4.8	42	8	NaN	NaN
410	APPLE 2021	Missing	Apple M1 Max	32	1024	0	Mac	8	NaN	309990	329900	5.0	3	1	NaN	NaN

411 rows × 16 columns

import matplotlib.pyplot as mat

label = ['Label1', 'Label2', 'Label3', 'Label4', 'Label5', 'Label6']
x_ticks=[30000,50000,70000,90000,110000,130000,150000,170000,200000,300000]

mat.plot(df1.latest_price,df1.laptop,'b.--' ,label='price plot')
mat.rcParams["figure.figsize"] = (20,12)
mat.xticks(ticks=x_ticks)
mat.show()

from pandas_profiling import ProfileReport

df1.dropna(how='all', axis=1, inplace=True)

laptopprofile = ProfileReport(df1)
laptopprofile.to_file(output_file="laptop_PROJECT_DS.html")

g4gb = df1.loc[df1.graphic_card_gb==4]
g4gb.to_csv('g4gb.csv')
print(g4gb)

            laptop processor_gnrtn      processor  ram_gb   ssd  hdd       os  \
284      MSI Sword            11th  Intel Core i7       8   512    0  Windows   
285       ASUS TUF            11th  Intel Core i5      16  1024    0  Windows   
286  DELL Inspiron            11th  Intel Core i5      16   512    0  Windows   
287      ASUS ASUS            11th  Intel Core i5      16   512    0  Windows   
288  DELL Inspiron            11th  Intel Core i5      16   512    0  Windows   
..             ...             ...            ...     ...   ...  ...      ...   
371       ASUS ROG         Missing    AMD Ryzen 9      16  1024    0  Windows   
372        HP OMEN         Missing    AMD Ryzen 9      16  1024    0  Windows   
373     ASUS Ryzen         Missing    AMD Ryzen 9      16  1024    0  Windows   
374        HP Envy            10th  Intel Core i7      16  1024    0  Windows   
375       ASUS ROG         Missing    AMD Ryzen 9      16  1024    0  Windows   

     graphic_card_gb  latest_price  old_price  star_rating  ratings  reviews  
284                4         86990     107990          0.0        0        0  
285                4         87990     126990          0.0        0        0  
286                4         88990      90605          3.8       19        2  
287                4         88990     129990          0.0        0        0  
288                4         89702          0          0.0        0        0  
..               ...           ...        ...          ...      ...      ...  
371                4        141990     189990          4.2       61       13  
372                4        141990     163739          0.0        0        0  
373                4        142990          0          0.0        0        0  
374                4        144690     173453          4.2       17        4  
375                4        144990     194990          0.0        0        0  

[92 rows x 13 columns]

mat.plot(g4gb.laptop,g4gb.latest_price)
mat.show()

<ipython-input-15-5bf1076e4a1d>:2: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
  mat.show()

mat.hist(df1.processor,color='red')

mat.title("Market hold by processor",size=50)
mat.show()

<ipython-input-16-b05e0e8de413>:4: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
  mat.show()

filepath2 = r"C:\Users\91623\OneDrive\Desktop\data12.txt"
df1= pd.read_csv(filepath2,delimiter='\t')

df1.sort_values('graphic_card_gb')

	laptop	processor_gnrtn	processor	ram_gb	ssd	hdd	os	graphic_card_gb	Unnamed: 8	latest_price	old_price	star_rating	ratings	reviews	Unnamed: 14	Unnamed: 15
0	HP Celeron	Missing	Intel Celeron Dual	8	256	0	Windows	0	NaN	32490	39288	4.0	58	6	NaN	NaN
154	DELL Vostro	11th	Intel Core i5	8	512	0	Windows	0	NaN	64190	85391	0.0	0	0	NaN	NaN
155	Lenovo IdeaPad	11th	Intel Core i5	16	512	0	Windows	0	NaN	64990	97890	0.0	0	0	NaN	NaN
156	ASUS VivoBook	11th	Intel Core i5	16	256	1024	Windows	0	NaN	64990	85990	4.7	10	1	NaN	NaN
157	ASUS VivoBook	11th	Intel Core i5	16	256	1024	Windows	0	NaN	64990	85990	4.3	328	51	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
401	APPLE 2021	Missing	Apple M1 Pro	16	1024	0	Mac	8	NaN	225990	239900	4.8	42	8	NaN	NaN
400	Lenovo Core	10th	Intel Core i7	16	512	0	Windows	8	NaN	211753	214760	0.0	0	0	NaN	NaN
409	ASUS ROG	12th	Intel Core i9	32	1024	0	Windows	8	NaN	299990	376990	0.0	0	0	NaN	NaN
403	APPLE 2021	Missing	Apple M1 Pro	16	1024	0	Mac	8	NaN	225990	239900	4.8	42	8	NaN	NaN
410	APPLE 2021	Missing	Apple M1 Max	32	1024	0	Mac	8	NaN	309990	329900	5.0	3	1	NaN	NaN

411 rows × 16 columns

df1.dropna(how='all', axis=1, inplace=True)

best laptop for coders

bfc1= df1.loc[df1.ssd>=256]
bfc1

	laptop	processor_gnrtn	processor	ram_gb	ssd	hdd	os	graphic_card_gb	latest_price	old_price	star_rating	ratings	reviews
0	HP Celeron	Missing	Intel Celeron Dual	8	256	0	Windows	0	32490	39288	4.0	58	6
1	DELL Vostro	Missing	AMD Ryzen 5	8	256	0	Windows	0	41990	59580	4.3	939	107
2	DELL Inspiron	Missing	AMD Ryzen 5	8	256	0	Windows	0	41990	49613	4.4	649	84
3	Infinix INBook	10th	Intel Core i5	8	512	0	Windows	0	42490	59999	4.1	383	65
4	acer Aspire	Missing	AMD Ryzen 5	8	512	0	Windows	0	43990	52999	0.0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...
406	Lenovo Legion	10th	Intel Core i7	16	1024	0	Windows	8	234990	349890	0.0	0	0
407	APPLE 2021	Missing	Apple M1 Pro	16	1024	0	Mac	8	243990	259900	4.8	42	8
408	MSI Stealth	12th	Intel Core i7	32	1024	0	Windows	8	279990	313990	0.0	0	0
409	ASUS ROG	12th	Intel Core i9	32	1024	0	Windows	8	299990	376990	0.0	0	0
410	APPLE 2021	Missing	Apple M1 Max	32	1024	0	Mac	8	309990	329900	5.0	3	1

401 rows × 13 columns

bfc2= bfc1.loc[bfc1.processor!='Intel Celeron Dual']
bfc2

	laptop	processor_gnrtn	processor	ram_gb	ssd	hdd	os	graphic_card_gb	latest_price	old_price	star_rating	ratings	reviews
1	DELL Vostro	Missing	AMD Ryzen 5	8	256	0	Windows	0	41990	59580	4.3	939	107
2	DELL Inspiron	Missing	AMD Ryzen 5	8	256	0	Windows	0	41990	49613	4.4	649	84
3	Infinix INBook	10th	Intel Core i5	8	512	0	Windows	0	42490	59999	4.1	383	65
4	acer Aspire	Missing	AMD Ryzen 5	8	512	0	Windows	0	43990	52999	0.0	0	0
5	DELL Inspiron	Missing	AMD Ryzen 5	8	256	0	Windows	0	44390	56573	4.0	276	24
...	...	...	...	...	...	...	...	...	...	...	...	...	...
406	Lenovo Legion	10th	Intel Core i7	16	1024	0	Windows	8	234990	349890	0.0	0	0
407	APPLE 2021	Missing	Apple M1 Pro	16	1024	0	Mac	8	243990	259900	4.8	42	8
408	MSI Stealth	12th	Intel Core i7	32	1024	0	Windows	8	279990	313990	0.0	0	0
409	ASUS ROG	12th	Intel Core i9	32	1024	0	Windows	8	299990	376990	0.0	0	0
410	APPLE 2021	Missing	Apple M1 Max	32	1024	0	Mac	8	309990	329900	5.0	3	1

400 rows × 13 columns

mat.scatter(bfc2.laptop,bfc2.latest_price)
mat.show()

<ipython-input-21-e33c301b93f5>:2: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
  mat.show()

bfc= bfc2.sort_values('latest_price')

bfc

	laptop	processor_gnrtn	processor	ram_gb	ssd	hdd	os	graphic_card_gb	latest_price	old_price	star_rating	ratings	reviews
1	DELL Vostro	Missing	AMD Ryzen 5	8	256	0	Windows	0	41990	59580	4.3	939	107
2	DELL Inspiron	Missing	AMD Ryzen 5	8	256	0	Windows	0	41990	49613	4.4	649	84
3	Infinix INBook	10th	Intel Core i5	8	512	0	Windows	0	42490	59999	4.1	383	65
4	acer Aspire	Missing	AMD Ryzen 5	8	512	0	Windows	0	43990	52999	0.0	0	0
5	DELL Inspiron	Missing	AMD Ryzen 5	8	256	0	Windows	0	44390	56573	4.0	276	24
...	...	...	...	...	...	...	...	...	...	...	...	...	...
406	Lenovo Legion	10th	Intel Core i7	16	1024	0	Windows	8	234990	349890	0.0	0	0
407	APPLE 2021	Missing	Apple M1 Pro	16	1024	0	Mac	8	243990	259900	4.8	42	8
408	MSI Stealth	12th	Intel Core i7	32	1024	0	Windows	8	279990	313990	0.0	0	0
409	ASUS ROG	12th	Intel Core i9	32	1024	0	Windows	8	299990	376990	0.0	0	0
410	APPLE 2021	Missing	Apple M1 Max	32	1024	0	Mac	8	309990	329900	5.0	3	1

400 rows × 13 columns

best laptop for gamer

bfg1= df1.loc[df1.os !='Mac' ]
bfg1

	laptop	processor_gnrtn	processor	ram_gb	ssd	hdd	os	graphic_card_gb	latest_price	old_price	star_rating	ratings	reviews
0	HP Celeron	Missing	Intel Celeron Dual	8	256	0	Windows	0	32490	39288	4.0	58	6
1	DELL Vostro	Missing	AMD Ryzen 5	8	256	0	Windows	0	41990	59580	4.3	939	107
2	DELL Inspiron	Missing	AMD Ryzen 5	8	256	0	Windows	0	41990	49613	4.4	649	84
3	Infinix INBook	10th	Intel Core i5	8	512	0	Windows	0	42490	59999	4.1	383	65
4	acer Aspire	Missing	AMD Ryzen 5	8	512	0	Windows	0	43990	52999	0.0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...
404	ASUS ExpertBook	10th	Intel Core i7	16	2048	0	Windows	8	233990	0	0.0	0	0
405	ASUS ROG	10th	Intel Core i7	32	1024	0	Windows	8	234990	324990	0.0	0	0
406	Lenovo Legion	10th	Intel Core i7	16	1024	0	Windows	8	234990	349890	0.0	0	0
408	MSI Stealth	12th	Intel Core i7	32	1024	0	Windows	8	279990	313990	0.0	0	0
409	ASUS ROG	12th	Intel Core i9	32	1024	0	Windows	8	299990	376990	0.0	0	0

384 rows × 13 columns

bfg2= bfg1.loc[bfg1.graphic_card_gb>=4]
bfg2
bfg2.processor.unique()

array(['Intel Core i7', 'Intel Core i5', 'AMD Ryzen 7', 'AMD Ryzen 5',
       'AMD Ryzen 9', 'Intel Core i9'], dtype=object)

bfg= bfg2.sort_values('graphic_card_gb')
bfg

	laptop	processor_gnrtn	processor	ram_gb	ssd	hdd	os	graphic_card_gb	latest_price	old_price	star_rating	ratings	reviews
284	MSI Sword	11th	Intel Core i7	8	512	0	Windows	4	86990	107990	0.0	0	0
351	ASUS ZenBook	10th	Intel Core i7	16	1024	0	Windows	4	124990	146990	4.6	17	1
349	Lenovo Yoga	11th	Intel Core i7	16	1024	0	Windows	4	122990	167890	3.0	4	2
348	DELL Inspiron	11th	Intel Core i7	16	1024	0	Windows	4	122410	0	0.0	0	0
347	HP OMEN	11th	Intel Core i7	16	1024	0	Windows	4	121990	138399	0.0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...
400	Lenovo Core	10th	Intel Core i7	16	512	0	Windows	8	211753	214760	0.0	0	0
404	ASUS ExpertBook	10th	Intel Core i7	16	2048	0	Windows	8	233990	0	0.0	0	0
405	ASUS ROG	10th	Intel Core i7	32	1024	0	Windows	8	234990	324990	0.0	0	0
406	Lenovo Legion	10th	Intel Core i7	16	1024	0	Windows	8	234990	349890	0.0	0	0
409	ASUS ROG	12th	Intel Core i9	32	1024	0	Windows	8	299990	376990	0.0	0	0

102 rows × 13 columns

mat.scatter(bfg.laptop,bfg.latest_price)
mat.show()

<ipython-input-27-ee83cd111c21>:2: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
  mat.show()

extracting best laptop for video editors graphic intensive

bfv1= df1.loc[df1.graphic_card_gb>=6]
bfv1

	laptop	processor_gnrtn	processor	ram_gb	ssd	hdd	os	graphic_card_gb	latest_price	old_price	star_rating	ratings	reviews
376	MSI Delta	Missing	AMD Ryzen 9	16	1024	0	Windows	6	146990	188990	0.0	0	0
377	Lenovo ThinkPad	10th	Intel Core i5	16	512	0	Windows	6	147000	0	0.0	0	0
378	APPLE MacBook	Missing	Apple M1 Processor	16	512	0	Mac	6	149990	163900	4.8	640	70
379	APPLE MacBook	Missing	Apple M1 Processor	8	1024	0	Mac	6	149990	163900	4.8	640	70
380	ASUS Ryzen	Missing	AMD Ryzen 9	8	1024	0	Windows	6	149990	0	0.0	0	0
381	Lenovo Legion	11th	Intel Core i7	16	1024	0	Windows	6	151098	224490	4.4	7	0
382	ASUS ExpertBook	10th	Intel Core i7	16	1024	0	Windows	6	154990	172990	5.0	3	0
383	APPLE MacBook	10th	Intel Core i5	16	512	0	Mac	6	159990	174900	4.4	32	5
384	MSI WF65	10th	Intel Core i7	16	256	1024	Windows	6	159990	179990	0.0	0	0
385	acer Predator	10th	Intel Core i7	16	2048	0	Windows	6	159990	160190	3.8	4	0
386	HP OMEN	Missing	AMD Ryzen 7	16	1024	0	Windows	6	159990	186348	4.8	17	2
387	Lenovo ThinkPad	10th	Intel Core i7	8	512	0	Windows	6	162299	178999	0.0	0	0
388	ASUS Vivobook	Missing	AMD Ryzen 9	16	1024	0	Windows	6	162990	0	0.0	0	0
389	ASUS ExpertBook	10th	Intel Core i7	16	1024	0	Windows	6	165990	202990	0.0	0	0
390	ASUS ExpertBook	10th	Intel Core i7	16	1024	0	Windows	6	167990	202990	0.0	0	0
391	APPLE MacBook	Missing	Apple M1 Processor	16	1024	0	Mac	6	169990	183900	4.8	640	70
392	acer Predator	11th	Intel Core i9	32	1024	0	Windows	6	169990	199999	0.0	0	0
393	HP OMEN	11th	Intel Core i7	16	1024	0	Windows	6	171990	197862	0.0	0	0
394	Lenovo ThinkPad	10th	Intel Core i7	16	512	0	Windows	6	174527	0	0.0	0	0
395	APPLE MacBook	10th	Intel Core i5	16	1024	0	Mac	6	179990	194900	4.7	31	3
396	APPLE MacBook	10th	Intel Core i5	16	1024	0	Mac	6	179990	194900	4.8	91	5
397	APPLE 2021	Missing	Apple M1 Pro	16	512	0	Mac	6	184990	194900	4.8	42	8
398	MSI Creator	11th	Intel Core i9	16	1024	0	Windows	6	192990	223890	0.0	0	0
399	ASUS ROG	Missing	AMD Ryzen 9	16	1024	0	Windows	8	199990	259990	4.7	9	2
400	Lenovo Core	10th	Intel Core i7	16	512	0	Windows	8	211753	214760	0.0	0	0
401	APPLE 2021	Missing	Apple M1 Pro	16	1024	0	Mac	8	225990	239900	4.8	42	8
402	APPLE 2021	Missing	Apple M1 Pro	16	512	0	Mac	8	225990	239900	4.8	42	8
403	APPLE 2021	Missing	Apple M1 Pro	16	1024	0	Mac	8	225990	239900	4.8	42	8
404	ASUS ExpertBook	10th	Intel Core i7	16	2048	0	Windows	8	233990	0	0.0	0	0
405	ASUS ROG	10th	Intel Core i7	32	1024	0	Windows	8	234990	324990	0.0	0	0
406	Lenovo Legion	10th	Intel Core i7	16	1024	0	Windows	8	234990	349890	0.0	0	0
407	APPLE 2021	Missing	Apple M1 Pro	16	1024	0	Mac	8	243990	259900	4.8	42	8
408	MSI Stealth	12th	Intel Core i7	32	1024	0	Windows	8	279990	313990	0.0	0	0
409	ASUS ROG	12th	Intel Core i9	32	1024	0	Windows	8	299990	376990	0.0	0	0
410	APPLE 2021	Missing	Apple M1 Max	32	1024	0	Mac	8	309990	329900	5.0	3	1

bfv2= bfv1.loc[df1.ram_gb >= 16 ]
bfv2

	laptop	processor_gnrtn	processor	ram_gb	ssd	hdd	os	graphic_card_gb	latest_price	old_price	star_rating	ratings	reviews
376	MSI Delta	Missing	AMD Ryzen 9	16	1024	0	Windows	6	146990	188990	0.0	0	0
377	Lenovo ThinkPad	10th	Intel Core i5	16	512	0	Windows	6	147000	0	0.0	0	0
378	APPLE MacBook	Missing	Apple M1 Processor	16	512	0	Mac	6	149990	163900	4.8	640	70
381	Lenovo Legion	11th	Intel Core i7	16	1024	0	Windows	6	151098	224490	4.4	7	0
382	ASUS ExpertBook	10th	Intel Core i7	16	1024	0	Windows	6	154990	172990	5.0	3	0
383	APPLE MacBook	10th	Intel Core i5	16	512	0	Mac	6	159990	174900	4.4	32	5
384	MSI WF65	10th	Intel Core i7	16	256	1024	Windows	6	159990	179990	0.0	0	0
385	acer Predator	10th	Intel Core i7	16	2048	0	Windows	6	159990	160190	3.8	4	0
386	HP OMEN	Missing	AMD Ryzen 7	16	1024	0	Windows	6	159990	186348	4.8	17	2
388	ASUS Vivobook	Missing	AMD Ryzen 9	16	1024	0	Windows	6	162990	0	0.0	0	0
389	ASUS ExpertBook	10th	Intel Core i7	16	1024	0	Windows	6	165990	202990	0.0	0	0
390	ASUS ExpertBook	10th	Intel Core i7	16	1024	0	Windows	6	167990	202990	0.0	0	0
391	APPLE MacBook	Missing	Apple M1 Processor	16	1024	0	Mac	6	169990	183900	4.8	640	70
392	acer Predator	11th	Intel Core i9	32	1024	0	Windows	6	169990	199999	0.0	0	0
393	HP OMEN	11th	Intel Core i7	16	1024	0	Windows	6	171990	197862	0.0	0	0
394	Lenovo ThinkPad	10th	Intel Core i7	16	512	0	Windows	6	174527	0	0.0	0	0
395	APPLE MacBook	10th	Intel Core i5	16	1024	0	Mac	6	179990	194900	4.7	31	3
396	APPLE MacBook	10th	Intel Core i5	16	1024	0	Mac	6	179990	194900	4.8	91	5
397	APPLE 2021	Missing	Apple M1 Pro	16	512	0	Mac	6	184990	194900	4.8	42	8
398	MSI Creator	11th	Intel Core i9	16	1024	0	Windows	6	192990	223890	0.0	0	0
399	ASUS ROG	Missing	AMD Ryzen 9	16	1024	0	Windows	8	199990	259990	4.7	9	2
400	Lenovo Core	10th	Intel Core i7	16	512	0	Windows	8	211753	214760	0.0	0	0
401	APPLE 2021	Missing	Apple M1 Pro	16	1024	0	Mac	8	225990	239900	4.8	42	8
402	APPLE 2021	Missing	Apple M1 Pro	16	512	0	Mac	8	225990	239900	4.8	42	8
403	APPLE 2021	Missing	Apple M1 Pro	16	1024	0	Mac	8	225990	239900	4.8	42	8
404	ASUS ExpertBook	10th	Intel Core i7	16	2048	0	Windows	8	233990	0	0.0	0	0
405	ASUS ROG	10th	Intel Core i7	32	1024	0	Windows	8	234990	324990	0.0	0	0
406	Lenovo Legion	10th	Intel Core i7	16	1024	0	Windows	8	234990	349890	0.0	0	0
407	APPLE 2021	Missing	Apple M1 Pro	16	1024	0	Mac	8	243990	259900	4.8	42	8
408	MSI Stealth	12th	Intel Core i7	32	1024	0	Windows	8	279990	313990	0.0	0	0
409	ASUS ROG	12th	Intel Core i9	32	1024	0	Windows	8	299990	376990	0.0	0	0
410	APPLE 2021	Missing	Apple M1 Max	32	1024	0	Mac	8	309990	329900	5.0	3	1

bfv= bfv2.sort_values('graphic_card_gb')
bfv

	laptop	processor_gnrtn	processor	ram_gb	ssd	hdd	os	graphic_card_gb	latest_price	old_price	star_rating	ratings	reviews
376	MSI Delta	Missing	AMD Ryzen 9	16	1024	0	Windows	6	146990	188990	0.0	0	0
398	MSI Creator	11th	Intel Core i9	16	1024	0	Windows	6	192990	223890	0.0	0	0
397	APPLE 2021	Missing	Apple M1 Pro	16	512	0	Mac	6	184990	194900	4.8	42	8
396	APPLE MacBook	10th	Intel Core i5	16	1024	0	Mac	6	179990	194900	4.8	91	5
395	APPLE MacBook	10th	Intel Core i5	16	1024	0	Mac	6	179990	194900	4.7	31	3
393	HP OMEN	11th	Intel Core i7	16	1024	0	Windows	6	171990	197862	0.0	0	0
392	acer Predator	11th	Intel Core i9	32	1024	0	Windows	6	169990	199999	0.0	0	0
391	APPLE MacBook	Missing	Apple M1 Processor	16	1024	0	Mac	6	169990	183900	4.8	640	70
390	ASUS ExpertBook	10th	Intel Core i7	16	1024	0	Windows	6	167990	202990	0.0	0	0
389	ASUS ExpertBook	10th	Intel Core i7	16	1024	0	Windows	6	165990	202990	0.0	0	0
394	Lenovo ThinkPad	10th	Intel Core i7	16	512	0	Windows	6	174527	0	0.0	0	0
386	HP OMEN	Missing	AMD Ryzen 7	16	1024	0	Windows	6	159990	186348	4.8	17	2
385	acer Predator	10th	Intel Core i7	16	2048	0	Windows	6	159990	160190	3.8	4	0
384	MSI WF65	10th	Intel Core i7	16	256	1024	Windows	6	159990	179990	0.0	0	0
383	APPLE MacBook	10th	Intel Core i5	16	512	0	Mac	6	159990	174900	4.4	32	5
382	ASUS ExpertBook	10th	Intel Core i7	16	1024	0	Windows	6	154990	172990	5.0	3	0
381	Lenovo Legion	11th	Intel Core i7	16	1024	0	Windows	6	151098	224490	4.4	7	0
378	APPLE MacBook	Missing	Apple M1 Processor	16	512	0	Mac	6	149990	163900	4.8	640	70
377	Lenovo ThinkPad	10th	Intel Core i5	16	512	0	Windows	6	147000	0	0.0	0	0
388	ASUS Vivobook	Missing	AMD Ryzen 9	16	1024	0	Windows	6	162990	0	0.0	0	0
408	MSI Stealth	12th	Intel Core i7	32	1024	0	Windows	8	279990	313990	0.0	0	0
407	APPLE 2021	Missing	Apple M1 Pro	16	1024	0	Mac	8	243990	259900	4.8	42	8
406	Lenovo Legion	10th	Intel Core i7	16	1024	0	Windows	8	234990	349890	0.0	0	0
405	ASUS ROG	10th	Intel Core i7	32	1024	0	Windows	8	234990	324990	0.0	0	0
404	ASUS ExpertBook	10th	Intel Core i7	16	2048	0	Windows	8	233990	0	0.0	0	0
400	Lenovo Core	10th	Intel Core i7	16	512	0	Windows	8	211753	214760	0.0	0	0
402	APPLE 2021	Missing	Apple M1 Pro	16	512	0	Mac	8	225990	239900	4.8	42	8
401	APPLE 2021	Missing	Apple M1 Pro	16	1024	0	Mac	8	225990	239900	4.8	42	8
399	ASUS ROG	Missing	AMD Ryzen 9	16	1024	0	Windows	8	199990	259990	4.7	9	2
409	ASUS ROG	12th	Intel Core i9	32	1024	0	Windows	8	299990	376990	0.0	0	0
403	APPLE 2021	Missing	Apple M1 Pro	16	1024	0	Mac	8	225990	239900	4.8	42	8
410	APPLE 2021	Missing	Apple M1 Max	32	1024	0	Mac	8	309990	329900	5.0	3	1

mat.scatter(bfv.laptop,bfv.latest_price)
mat.show()

<ipython-input-31-027f3437eb03>:2: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
  mat.show()

bfvf = bfv.drop([385,377,407])

bfg.corr()

	ram_gb	ssd	hdd	graphic_card_gb	latest_price	old_price	star_rating	ratings	reviews
ram_gb	1.000000	0.244890	-0.069869	0.421031	0.510695	0.493190	0.016528	0.015826	0.016085
ssd	0.244890	1.000000	-0.383506	0.322946	0.466878	0.267141	-0.084586	-0.202729	-0.199882
hdd	-0.069869	-0.383506	1.000000	-0.036387	-0.092261	-0.094051	0.101527	0.497079	0.497876
graphic_card_gb	0.421031	0.322946	-0.036387	1.000000	0.883348	0.427326	-0.187894	-0.104086	-0.104204
latest_price	0.510695	0.466878	-0.092261	0.883348	1.000000	0.554149	-0.242267	-0.094059	-0.088990
old_price	0.493190	0.267141	-0.094051	0.427326	0.554149	1.000000	-0.010367	-0.018717	-0.013828
star_rating	0.016528	-0.084586	0.101527	-0.187894	-0.242267	-0.010367	1.000000	0.275477	0.273368
ratings	0.015826	-0.202729	0.497079	-0.104086	-0.094059	-0.018717	0.275477	1.000000	0.997977
reviews	0.016085	-0.199882	0.497876	-0.104204	-0.088990	-0.013828	0.273368	0.997977	1.000000

import seaborn as sns

sns.pairplot(bfg,hue='laptop')

C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\91623\anaconda3\lib\site-packages\seaborn\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)

<seaborn.axisgrid.PairGrid at 0x1e454b09100>

corrmat=bfg.corr()
top_corr=corrmat.index
mat.figure(figsize=(20,20))
g= sns.heatmap(bfg[top_corr].corr(),annot= True,cmap="RdYlGn")

sns.histplot(df1['old_price'],color='red',label='Old Price')
sns.histplot(df1['latest_price'],label='New Price')
mat.legend()
mat.title('Histogram of Old and New Price')

Text(0.5, 1.0, 'Histogram of Old and New Price')

mat.scatter(df1.latest_price,df1.laptop)
mat.show()

<ipython-input-38-ea6ce9cdf698>:2: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
  mat.show()

mosbfc= bfc.loc[bfc.os== "Mac"]
mosbfc

	laptop	processor_gnrtn	processor	ram_gb	ssd	hdd	os	graphic_card_gb	latest_price	old_price	star_rating	ratings	reviews
280	APPLE 2020	Missing	Apple M1 Processor	8	256	0	Mac	2	85990	92900	4.7	6725	629
281	APPLE 2020	Missing	Apple M1 Processor	8	256	0	Mac	2	85990	92900	4.7	6725	629
320	APPLE 2020	Missing	Apple M1 Processor	8	512	0	Mac	4	101990	117900	4.7	809	91
321	APPLE 2020	Missing	Apple M1 Processor	8	512	0	Mac	4	103990	117900	4.7	809	91
322	APPLE MacBook	Missing	Apple M1 Processor	16	256	0	Mac	4	104990	112900	4.7	45	4
323	APPLE MacBook	Missing	Apple M1 Processor	16	256	0	Mac	4	104990	112900	4.7	45	4
324	APPLE MacBook	Missing	Apple M1 Processor	8	512	0	Mac	4	104990	110900	4.7	6725	629
326	APPLE MacBook	Missing	Apple M1 Processor	8	512	0	Mac	4	104990	110900	4.7	6725	629
335	APPLE 2020	Missing	Apple M1 Processor	8	256	0	Mac	4	112990	122900	4.8	640	70
336	APPLE 2020	Missing	Apple M1 Processor	8	256	0	Mac	4	112990	122900	4.8	640	70
350	APPLE MacBook	Missing	Apple M1 Processor	16	512	0	Mac	4	124990	132900	4.7	45	4
352	APPLE 2020	Missing	Apple M1 Processor	8	512	0	Mac	4	125990	142900	4.6	399	45
353	APPLE MacBook	Missing	Apple M1 Processor	16	512	0	Mac	4	128990	137900	4.7	45	4
361	APPLE 2020	Missing	Apple M1 Processor	8	512	0	Mac	4	131990	142900	4.6	399	45
363	APPLE MacBook	Missing	Apple M1 Processor	16	256	0	Mac	4	132990	149900	4.8	640	70
378	APPLE MacBook	Missing	Apple M1 Processor	16	512	0	Mac	6	149990	163900	4.8	640	70
379	APPLE MacBook	Missing	Apple M1 Processor	8	1024	0	Mac	6	149990	163900	4.8	640	70
383	APPLE MacBook	10th	Intel Core i5	16	512	0	Mac	6	159990	174900	4.4	32	5
391	APPLE MacBook	Missing	Apple M1 Processor	16	1024	0	Mac	6	169990	183900	4.8	640	70
396	APPLE MacBook	10th	Intel Core i5	16	1024	0	Mac	6	179990	194900	4.8	91	5
395	APPLE MacBook	10th	Intel Core i5	16	1024	0	Mac	6	179990	194900	4.7	31	3
397	APPLE 2021	Missing	Apple M1 Pro	16	512	0	Mac	6	184990	194900	4.8	42	8
401	APPLE 2021	Missing	Apple M1 Pro	16	1024	0	Mac	8	225990	239900	4.8	42	8
402	APPLE 2021	Missing	Apple M1 Pro	16	512	0	Mac	8	225990	239900	4.8	42	8
403	APPLE 2021	Missing	Apple M1 Pro	16	1024	0	Mac	8	225990	239900	4.8	42	8
407	APPLE 2021	Missing	Apple M1 Pro	16	1024	0	Mac	8	243990	259900	4.8	42	8
410	APPLE 2021	Missing	Apple M1 Max	32	1024	0	Mac	8	309990	329900	5.0	3	1

mosbfv= bfv.loc[bfv.os=="Mac"]
mosbfv

	laptop	processor_gnrtn	processor	ram_gb	ssd	hdd	os	graphic_card_gb	latest_price	old_price	star_rating	ratings	reviews
397	APPLE 2021	Missing	Apple M1 Pro	16	512	0	Mac	6	184990	194900	4.8	42	8
396	APPLE MacBook	10th	Intel Core i5	16	1024	0	Mac	6	179990	194900	4.8	91	5
395	APPLE MacBook	10th	Intel Core i5	16	1024	0	Mac	6	179990	194900	4.7	31	3
391	APPLE MacBook	Missing	Apple M1 Processor	16	1024	0	Mac	6	169990	183900	4.8	640	70
383	APPLE MacBook	10th	Intel Core i5	16	512	0	Mac	6	159990	174900	4.4	32	5
378	APPLE MacBook	Missing	Apple M1 Processor	16	512	0	Mac	6	149990	163900	4.8	640	70
407	APPLE 2021	Missing	Apple M1 Pro	16	1024	0	Mac	8	243990	259900	4.8	42	8
402	APPLE 2021	Missing	Apple M1 Pro	16	512	0	Mac	8	225990	239900	4.8	42	8
401	APPLE 2021	Missing	Apple M1 Pro	16	1024	0	Mac	8	225990	239900	4.8	42	8
403	APPLE 2021	Missing	Apple M1 Pro	16	1024	0	Mac	8	225990	239900	4.8	42	8
410	APPLE 2021	Missing	Apple M1 Max	32	1024	0	Mac	8	309990	329900	5.0	3	1

mat.scatter(bfv.laptop,bfv.latest_price)
mat.scatter(mosbfv.laptop,mosbfv.latest_price)
mat.show()

<ipython-input-41-032eb1b60ac9>:3: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
  mat.show()

wosbfc= bfc.loc[bfc.os!="Mac"]
wosbfc

	laptop	processor_gnrtn	processor	ram_gb	ssd	hdd	os	graphic_card_gb	latest_price	old_price	star_rating	ratings	reviews
1	DELL Vostro	Missing	AMD Ryzen 5	8	256	0	Windows	0	41990	59580	4.3	939	107
2	DELL Inspiron	Missing	AMD Ryzen 5	8	256	0	Windows	0	41990	49613	4.4	649	84
3	Infinix INBook	10th	Intel Core i5	8	512	0	Windows	0	42490	59999	4.1	383	65
4	acer Aspire	Missing	AMD Ryzen 5	8	512	0	Windows	0	43990	52999	0.0	0	0
5	DELL Inspiron	Missing	AMD Ryzen 5	8	256	0	Windows	0	44390	56573	4.0	276	24
...	...	...	...	...	...	...	...	...	...	...	...	...	...
404	ASUS ExpertBook	10th	Intel Core i7	16	2048	0	Windows	8	233990	0	0.0	0	0
405	ASUS ROG	10th	Intel Core i7	32	1024	0	Windows	8	234990	324990	0.0	0	0
406	Lenovo Legion	10th	Intel Core i7	16	1024	0	Windows	8	234990	349890	0.0	0	0
408	MSI Stealth	12th	Intel Core i7	32	1024	0	Windows	8	279990	313990	0.0	0	0
409	ASUS ROG	12th	Intel Core i9	32	1024	0	Windows	8	299990	376990	0.0	0	0

373 rows × 13 columns

mosbfv= bfv.loc[bfv.os=="Mac"]
mosbfv

	laptop	processor_gnrtn	processor	ram_gb	ssd	hdd	os	graphic_card_gb	latest_price	old_price	star_rating	ratings	reviews
397	APPLE 2021	Missing	Apple M1 Pro	16	512	0	Mac	6	184990	194900	4.8	42	8
396	APPLE MacBook	10th	Intel Core i5	16	1024	0	Mac	6	179990	194900	4.8	91	5
395	APPLE MacBook	10th	Intel Core i5	16	1024	0	Mac	6	179990	194900	4.7	31	3
391	APPLE MacBook	Missing	Apple M1 Processor	16	1024	0	Mac	6	169990	183900	4.8	640	70
383	APPLE MacBook	10th	Intel Core i5	16	512	0	Mac	6	159990	174900	4.4	32	5
378	APPLE MacBook	Missing	Apple M1 Processor	16	512	0	Mac	6	149990	163900	4.8	640	70
407	APPLE 2021	Missing	Apple M1 Pro	16	1024	0	Mac	8	243990	259900	4.8	42	8
402	APPLE 2021	Missing	Apple M1 Pro	16	512	0	Mac	8	225990	239900	4.8	42	8
401	APPLE 2021	Missing	Apple M1 Pro	16	1024	0	Mac	8	225990	239900	4.8	42	8
403	APPLE 2021	Missing	Apple M1 Pro	16	1024	0	Mac	8	225990	239900	4.8	42	8
410	APPLE 2021	Missing	Apple M1 Max	32	1024	0	Mac	8	309990	329900	5.0	3	1

mat.scatter(bfc.laptop,bfc.latest_price)
mat.scatter(mosbfc.laptop,mosbfc.latest_price)
mat.show()

<ipython-input-44-6d711f061959>:3: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
  mat.show()

name = input("HELLO, WHATS YOUR NAME? ")
print(name,", I will guied which LAPTOP you should buy")
budget = int(input("Whats your budget?"))
print("your budget is",budget)
print("1: coder or normal use")
print("2: Gamer")
print("3: Graphic oriented")
inp = int(input("Enter a number: "))

if inp == 1:
    print("1: Windows")
    print("2: Macos")
    os= int(input("which os you want Windows or Macos?"))
    if os ==1:
        bl= wosbfc.loc[wosbfc.latest_price<=budget]
        x = bl.sort_values(['latest_price'], ascending=False)
        print("\n\n\n\n")
        y= x[['laptop','processor','ram_gb','graphic_card_gb','latest_price']]
        z= y.head()
        print(z)
    elif os==2:
        bl= mosbfc.loc[mosbfc.latest_price<=budget]
        x = bl.sort_values(['latest_price'], ascending=False)
        print("\n\n\n\n")
        y= x[['laptop','processor','ram_gb','graphic_card_gb','latest_price']]
        z= y.head()
        print(z)
    else:
        print("Invalid input!")   
    
    
elif inp == 2:
        bl= bfg.loc[bfg.latest_price<=budget]
        x = bl.sort_values(['latest_price'], ascending=False)
        print("\n\n\n\n")
        y= x[['laptop','processor','ram_gb','graphic_card_gb','latest_price']]
        z= y.head()
        print(z)
    
elif inp == 3:
    if(budget>=140000):
        print("1: Windows")
        print("2: Macos")
    
        os= int(input("which os you want Windows or Macos?"))
        if os ==1:
            bl= wosbfv.loc[wosbfv.latest_price<=budget]
            x = bl.sort_values(['latest_price'], ascending=False)
            print("\n\n\n\n")
            y= x[['laptop','processor','ram_gb','graphic_card_gb','latest_price']]
            z= y.head()
            print(z)
        elif os==2:
            bl= mosbfv.loc[mosbfv.latest_price<=budget]
            x = bl.sort_values(['latest_price'], ascending=False)
            print("\n\n\n\n")
            y= x[['laptop','processor','ram_gb','graphic_card_gb','latest_price']]
            z= y.head()
            print(z)
         
        
        else:
            print("Invalid input!")  
    else:
        print("FOR A VERY POWERFUL LAPTOP OF HIGH GRAPHIC CAPCITY BUDGET SHOULD BE ATLEAST 140000 THANKYOU YOU CAN OPT A GOOD LAPTOP FROM GAME CATEGORY")
        
else:
    print("Invalid input!")

---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-45-8d1594503328> in <module>
----> 1 name = input("HELLO, WHATS YOUR NAME? ")
      2 print(name,", I will guied which LAPTOP you should buy")
      3 budget = int(input("Whats your budget?"))
      4 print("your budget is",budget)
      5 print("1: coder or normal use")

~\anaconda3\lib\site-packages\ipykernel\kernelbase.py in raw_input(self, prompt)
    858                 "raw_input was called, but this frontend does not support input requests."
    859             )
--> 860         return self._input_request(str(prompt),
    861             self._parent_ident,
    862             self._parent_header,

~\anaconda3\lib\site-packages\ipykernel\kernelbase.py in _input_request(self, prompt, ident, parent, password)
    902             except KeyboardInterrupt:
    903                 # re-raise KeyboardInterrupt, to truncate traceback
--> 904                 raise KeyboardInterrupt("Interrupted by user") from None
    905             except Exception as e:
    906                 self.log.warning("Invalid Message:", exc_info=True)

KeyboardInterrupt: Interrupted by user

import plotly.express as px

import plotly.express as px

fig = px.sunburst(df1, path=['laptop', 'processor','latest_price'], branchvalues='total',color='latest_price',template='plotly',title='price over Brand and Processor')
fig.show()

fig = px.sunburst(bfg, path=['laptop', 'processor','latest_price'], branchvalues='total',color='latest_price',template='plotly',title='price over Brand and Processor')
fig.show()

fig = px.sunburst(bfv, path=['laptop', 'processor','latest_price'], branchvalues='total',color='latest_price',template='plotly',title='price over Brand and Processor')
fig.show()

import plotly.graph_objects as go
fig = px.scatter(df1, x="latest_price", y="ratings", color="ssd",
    size="reviews", size_max=45, log_x=True)

fig.update_layout(legend=dict(
    yanchor="top",
    y=0.99,
    xanchor="left",
    x=0.01
))

fig.show()

import plotly.graph_objects as go
fig = px.scatter(bfc, x="latest_price", y="ratings", color="ssd",
    size="reviews", size_max=45, log_x=True)

fig.update_layout(legend=dict(
    yanchor="top",
    y=0.99,
    xanchor="left",
    x=0.01
))

fig.show()

fig = px.treemap(df1, path=[px.Constant("Laptop"), 'laptop', 'processor'], values='latest_price',
                  color='latest_price', hover_data=['os'],
                  color_continuous_scale='RdBu',
                  color_continuous_midpoint=nup.average(df1['latest_price'], weights=df1['graphic_card_gb']),title='ALL LAPTOPS')
fig.update_layout(margin = dict(t=50, l=25, r=25, b=25))
fig.show()

fig = px.treemap(bfg, path=[px.Constant("Laptop"), 'laptop', 'processor'], values='latest_price',
                  color='latest_price', hover_data=['os'],
                  color_continuous_scale='RdBu',
                  color_continuous_midpoint=nup.average(df1['latest_price'], weights=df1['graphic_card_gb']),title='BEST LAPTOPS FOR GAMER')
fig.update_layout(margin = dict(t=50, l=25, r=25, b=25))
fig.show()

fig = px.treemap(bfv, path=[px.Constant("Laptop"), 'laptop', 'processor'], values='latest_price',
                  color='latest_price', hover_data=['os'],
                  color_continuous_scale='RdBu',
                  color_continuous_midpoint=nup.average(df1['latest_price'], weights=df1['graphic_card_gb']),title='BEST LAPTOPS')
fig.update_layout(margin = dict(t=50, l=25, r=25, b=25))
fig.show()

fig = px.treemap(bfc, path=[px.Constant("Laptop"), 'laptop', 'processor'], values='latest_price',
                  color='latest_price', hover_data=['os'],
                  color_continuous_scale='RdBu',
                  color_continuous_midpoint=nup.average(df1['latest_price'], weights=df1['graphic_card_gb']),title='LAPTOP FOR CODERS')
fig.update_layout(margin = dict(t=50, l=25, r=25, b=25))
fig.show()

thing is i am done with project trying to add some ml model for pridicting price

df1

	laptop	processor_gnrtn	processor	ram_gb	ssd	hdd	os	graphic_card_gb	latest_price	old_price	star_rating	ratings	reviews
0	HP Celeron	Missing	Intel Celeron Dual	8	256	0	Windows	0	32490	39288	4.0	58	6
1	DELL Vostro	Missing	AMD Ryzen 5	8	256	0	Windows	0	41990	59580	4.3	939	107
2	DELL Inspiron	Missing	AMD Ryzen 5	8	256	0	Windows	0	41990	49613	4.4	649	84
3	Infinix INBook	10th	Intel Core i5	8	512	0	Windows	0	42490	59999	4.1	383	65
4	acer Aspire	Missing	AMD Ryzen 5	8	512	0	Windows	0	43990	52999	0.0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...
406	Lenovo Legion	10th	Intel Core i7	16	1024	0	Windows	8	234990	349890	0.0	0	0
407	APPLE 2021	Missing	Apple M1 Pro	16	1024	0	Mac	8	243990	259900	4.8	42	8
408	MSI Stealth	12th	Intel Core i7	32	1024	0	Windows	8	279990	313990	0.0	0	0
409	ASUS ROG	12th	Intel Core i9	32	1024	0	Windows	8	299990	376990	0.0	0	0
410	APPLE 2021	Missing	Apple M1 Max	32	1024	0	Mac	8	309990	329900	5.0	3	1

411 rows × 13 columns

df1.sort_values('latest_price')

	laptop	processor_gnrtn	processor	ram_gb	ssd	hdd	os	graphic_card_gb	latest_price	old_price	star_rating	ratings	reviews
0	HP Celeron	Missing	Intel Celeron Dual	8	256	0	Windows	0	32490	39288	4.0	58	6
1	DELL Vostro	Missing	AMD Ryzen 5	8	256	0	Windows	0	41990	59580	4.3	939	107
2	DELL Inspiron	Missing	AMD Ryzen 5	8	256	0	Windows	0	41990	49613	4.4	649	84
3	Infinix INBook	10th	Intel Core i5	8	512	0	Windows	0	42490	59999	4.1	383	65
4	acer Aspire	Missing	AMD Ryzen 5	8	512	0	Windows	0	43990	52999	0.0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...
406	Lenovo Legion	10th	Intel Core i7	16	1024	0	Windows	8	234990	349890	0.0	0	0
407	APPLE 2021	Missing	Apple M1 Pro	16	1024	0	Mac	8	243990	259900	4.8	42	8
408	MSI Stealth	12th	Intel Core i7	32	1024	0	Windows	8	279990	313990	0.0	0	0
409	ASUS ROG	12th	Intel Core i9	32	1024	0	Windows	8	299990	376990	0.0	0	0
410	APPLE 2021	Missing	Apple M1 Max	32	1024	0	Mac	8	309990	329900	5.0	3	1

411 rows × 13 columns

mat.scatter(df1.graphic_card_gb,df1.latest_price)
mat.show()

<ipython-input-58-b68eeda18b35>:2: UserWarning:

Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.

mat.scatter(df1.ram_gb,df1.latest_price)
mat.show()

<ipython-input-59-3c4dcaa0d215>:2: UserWarning:

Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.

mat.scatter(df1.latest_price,df1.processor)
mat.show()

<ipython-input-60-10d9b21e3930>:2: UserWarning:

Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.

mat.scatter(df1.ssd,df1.latest_price)
mat.show()

<ipython-input-61-8727a12e1f9d>:2: UserWarning:

Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.

#model
x= (df1[['ssd']])
y=(df1[['latest_price']])
x.shape
y.shape

(411, 1)

from sklearn.model_selection import train_test_split

x_train,y_train,x_test,y_test=train_test_split(x,y,test_size=0.2)

from sklearn.linear_model import LinearRegression

lr=LinearRegression()

lr.fit(x_train,y_train)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-67-78e42af1b936> in <module>
----> 1 lr.fit(x_train,y_train)

~\anaconda3\lib\site-packages\sklearn\linear_model\_base.py in fit(self, X, y, sample_weight)
    503 
    504         n_jobs_ = self.n_jobs
--> 505         X, y = self._validate_data(X, y, accept_sparse=['csr', 'csc', 'coo'],
    506                                    y_numeric=True, multi_output=True)
    507 

~\anaconda3\lib\site-packages\sklearn\base.py in _validate_data(self, X, y, reset, validate_separately, **check_params)
    430                 y = check_array(y, **check_y_params)
    431             else:
--> 432                 X, y = check_X_y(X, y, **check_params)
    433             out = X, y
    434 

~\anaconda3\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs)
     70                           FutureWarning)
     71         kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 72         return f(**kwargs)
     73     return inner_f
     74 

~\anaconda3\lib\site-packages\sklearn\utils\validation.py in check_X_y(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)
    810         y = y.astype(np.float64)
    811 
--> 812     check_consistent_length(X, y)
    813 
    814     return X, y

~\anaconda3\lib\site-packages\sklearn\utils\validation.py in check_consistent_length(*arrays)
    253     uniques = np.unique(lengths)
    254     if len(uniques) > 1:
--> 255         raise ValueError("Found input variables with inconsistent numbers of"
    256                          " samples: %r" % [int(l) for l in lengths])
    257 

ValueError: Found input variables with inconsistent numbers of samples: [328, 83]